{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 3 2]\n",
      " [5 6 7]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "array = np.array([[4,3,2],\n",
    "             [5,6,7]])\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of dim: 2\n",
      "shape: (2, 3)\n",
      "size: 6\n"
     ]
    }
   ],
   "source": [
    "print('number of dim:', array.ndim)\n",
    "print('shape:', array.shape)\n",
    "print('size:', array.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(12).reshape((3,4))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1.  ]\n",
      " [  3.25]\n",
      " [  5.5 ]\n",
      " [  7.75]\n",
      " [ 10.  ]]\n"
     ]
    }
   ],
   "source": [
    "a = np.linspace(1,10,5).reshape((5,1))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-5.44021111  9.12945251 -9.88031624  7.4511316 ]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([10,20,30,40])\n",
    "b = np.arange(4)\n",
    "print(10*np.sin(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n",
      "[ True  True  True False]\n"
     ]
    }
   ],
   "source": [
    "print(b)\n",
    "print(b<3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.array([[10,20],\n",
    "              [30,40]])\n",
    "b = np.arange(4).reshape((2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0  20]\n",
      " [ 60 120]]\n"
     ]
    }
   ],
   "source": [
    "print(a*b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 40  70]\n",
      " [ 80 150]]\n"
     ]
    }
   ],
   "source": [
    "c_ = np.dot(a, b)\n",
    "print(c_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 40  70]\n",
      " [ 80 150]]\n"
     ]
    }
   ],
   "source": [
    "print(a.dot(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.61803694  0.85397425  0.27896167  0.95457785]\n",
      " [ 0.13966112  0.67161935  0.79413807  0.73412336]]\n",
      "5.04509261436\n"
     ]
    }
   ],
   "source": [
    "a = np.random.random((2,4))\n",
    "print(a)\n",
    "print(np.sum(a,axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "A = np.arange(0,12).reshape((3,4))\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(A,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.5"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  5,  9, 14, 20, 27, 35, 44, 54, 65, 77, 90])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1],\n",
       "       [1, 1, 1],\n",
       "       [1, 1, 1]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]),\n",
       " array([1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nonzero(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  4,  8],\n",
       "       [ 1,  5,  9],\n",
       "       [ 2,  6, 10],\n",
       "       [ 3,  7, 11]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  4,  8],\n",
       "       [ 1,  5,  9],\n",
       "       [ 2,  6, 10],\n",
       "       [ 3,  7, 11]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 80,  92, 104, 116],\n",
       "       [ 92, 107, 122, 137],\n",
       "       [104, 122, 140, 158],\n",
       "       [116, 137, 158, 179]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(A.T).dot(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 5, 5, 5],\n",
       "       [5, 5, 6, 7],\n",
       "       [8, 9, 9, 9]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.clip(A,5,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.,  5.,  6.,  7.])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(A,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.5,  5.5,  9.5])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(A,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3  4  5  6]\n",
      " [ 7  8  9 10]\n",
      " [11 12 13 14]]\n"
     ]
    }
   ],
   "source": [
    "A = np.arange(3,15).reshape(3,4)\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 12\n"
     ]
    }
   ],
   "source": [
    "print(A[2,1],A[2][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7  8  9 10]\n"
     ]
    }
   ],
   "source": [
    "print(A[1,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4  8 12]\n"
     ]
    }
   ],
   "source": [
    "print(A[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9]\n"
     ]
    }
   ],
   "source": [
    "print(A[1,2:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 5 6]\n",
      "[ 7  8  9 10]\n",
      "[11 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "for row in A:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3  7 11]\n",
      "[ 4  8 12]\n",
      "[ 5  9 13]\n",
      "[ 6 10 14]\n"
     ]
    }
   ],
   "source": [
    "for column in A.T:\n",
    "    print(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n"
     ]
    }
   ],
   "source": [
    "for item in A.flat:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3  4  5  6  7  8  9 10 11 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "print(A.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A = np.array([1,1,1])\n",
    "B = np.array([2,2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1]\n",
      " [2 2 2]]\n"
     ]
    }
   ],
   "source": [
    "c=np.vstack((A,B))\n",
    "print(c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape\n",
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(np.hstack((A,B)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.T\n",
    "A\n",
    "A==A.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 1)\n",
      "[[1]\n",
      " [1]\n",
      " [1]]\n",
      "[[1 1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(A.shape)\n",
    "print(A)\n",
    "print(A.T)\n",
    "A[np.newaxis,:].shape\n",
    "A[:,np.newaxis].shape\n",
    "A=np.array([1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [1],\n",
       "       [1]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,1,1])[:,np.newaxis]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((A,B,B,A),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "A = np.arange(12).reshape((3,4))\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0, 1],\n",
       "        [4, 5],\n",
       "        [8, 9]]), array([[ 2,  3],\n",
       "        [ 6,  7],\n",
       "        [10, 11]])]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.split(A,2,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.randint(0, 5, [3,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0 1 4]\n",
      "  [0 3 0]]\n",
      "\n",
      " [[4 1 1]\n",
      "  [0 2 1]]\n",
      "\n",
      " [[2 0 3]\n",
      "  [3 2 1]]]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0, 4, 2],\n",
       "        [0, 0, 3]],\n",
       "\n",
       "       [[1, 1, 0],\n",
       "        [3, 2, 2]],\n",
       "\n",
       "       [[4, 1, 3],\n",
       "        [0, 1, 1]]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 4, 4],\n",
       "       [4, 3, 2],\n",
       "       [5, 2, 4]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "A = np.arange(12).reshape((3,4))\n",
    "\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0, 1],\n",
       "        [4, 5],\n",
       "        [8, 9]]), array([[ 2],\n",
       "        [ 6],\n",
       "        [10]]), array([[ 3],\n",
       "        [ 7],\n",
       "        [11]])]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_split(A,3,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vsplit(A,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "      <td>6</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1   2   3\n",
       "0  0  1   2   3\n",
       "1  4  5   6   7\n",
       "2  8  9  10  11"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.arange(12).reshape((3,4)))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "        text-align: left;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b   c   d\n",
       "1  0  1   2   3\n",
       "2  4  5   6   7\n",
       "3  8  9  10  11"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(np.arange(12).reshape((3,4)),index = np.arange(1,4),columns = ['a','b','c','d'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = np.arange(1,4)\n",
    "type(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1.0\n",
       "1     3.0\n",
       "2     4.0\n",
       "3     NaN\n",
       "4    44.0\n",
       "5     1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,4,np.nan,44,1])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
       "               '2016-01-05', '2016-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20160101',periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    int64\n",
       "b    int64\n",
       "c    int64\n",
       "d    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 3], dtype='int64')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "      <th>d</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         a    b     c     d\n",
       "count  3.0  3.0   3.0   3.0\n",
       "mean   4.0  5.0   6.0   7.0\n",
       "std    4.0  4.0   4.0   4.0\n",
       "min    0.0  1.0   2.0   3.0\n",
       "25%    2.0  3.0   4.0   5.0\n",
       "50%    4.0  5.0   6.0   7.0\n",
       "75%    6.0  7.0   8.0   9.0\n",
       "max    8.0  9.0  10.0  11.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   a  b   c   d\n",
       "1  0  1   2   3\n",
       "2  4  5   6   7\n",
       "3  8  9  10  11"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "      <th>c</th>\n",
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       "      <th>d</th>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "   1  2   3\n",
       "a  0  4   8\n",
       "b  1  5   9\n",
       "c  2  6  10\n",
       "d  3  7  11"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "\n",
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       "      <th>3</th>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   a  b   c   d\n",
       "3  8  9  10  11\n",
       "2  4  5   6   7\n",
       "1  0  1   2   3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_index(axis = 0,ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "      <td>11</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b   c   d\n",
       "1  0  1   2   3\n",
       "2  4  5   6   7\n",
       "3  8  9  10  11"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    int64\n",
       "b    int64\n",
       "c    int64\n",
       "d    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
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  {
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   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   a  b   c   d\n",
       "3  8  9  10  11\n",
       "2  4  5   6   7\n",
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     "execution_count": 20,
     "metadata": {},
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   "source": [
    "df2.sort_values(by = 'a', ascending=False)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
       "               '2016-01-05', '2016-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "dates"
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   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "             A   B   C   D\n",
       "2016-01-01   0   1   2   3\n",
       "2016-01-02   4   5   6   7\n",
       "2016-01-03   8   9  10  11\n",
       "2016-01-04  12  13  14  15\n",
       "2016-01-05  16  17  18  19\n",
       "2016-01-06  20  21  22  23"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.DataFrame(np.arange(24).reshape((6,4)),index = dates,columns=['A','B','C','D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "df.iloc[2,2] = 1111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "             A   B     C   D\n",
       "2016-01-01   0   1     2   3\n",
       "2016-01-02   4   5     6   7\n",
       "2016-01-03   8   9  1111  11\n",
       "2016-01-04  12  13    14  15\n",
       "2016-01-05  16  17    18  19\n",
       "2016-01-06  20  21    22  23"
      ]
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " df.loc['20160101','B']=2222"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2222</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
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       "      <th>2016-01-05</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A     B     C   D  F   G  E\n",
       "2016-01-01   0  2222     2   3  0 NaN  1\n",
       "2016-01-02   4     5     6   7  1 NaN  2\n",
       "2016-01-03   8     9  1111  11  2 NaN  3\n",
       "2016-01-04  12    13    14  15  3 NaN  4\n",
       "2016-01-05  16    17    18  19  4 NaN  5\n",
       "2016-01-06   0    21    22  23  5 NaN  6"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.A[df.A>16]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['F']=np.arange(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['G']=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['E'] = pd.Series([1,2,3,4,5,6], index = pd.date_range('20160101', periods=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>4</td>\n",
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       "      <td>6</td>\n",
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       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1111</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A     B     C   D  E  F   G\n",
       "2016-01-01   0  2222     2   3  1  0 NaN\n",
       "2016-01-02   4     5     6   7  2  1 NaN\n",
       "2016-01-03   8     9  1111  11  3  2 NaN\n",
       "2016-01-04  12    13    14  15  4  3 NaN\n",
       "2016-01-05  16    17    18  19  5  4 NaN\n",
       "2016-01-06   0    21    22  23  6  5 NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.DataFrame(np.arange(24).reshape((4,6)), index = pd.date_range('20180101', periods=4), columns=pd.Series(np.arange(6,12)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6</td>\n",
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       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            6   7   8   9   10  11\n",
       "2018-01-01   0   1   2   3   4   5\n",
       "2018-01-02   6   7   8   9  10  11\n",
       "2018-01-03  12  13  14  15  16  17\n",
       "2018-01-04  18  19  20  21  22  23"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data2 = pd.DataFrame(np.arange(24).reshape((4,6)), index = pd.date_range('20170101', periods=4), columns=pd.Series(np.arange(6,12)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data3=pd.concat([data,data2], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data3.to_csv('study.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data3.to_pickle('study.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            6   7   8   9   10  11\n",
       "2018-01-01   0   1   2   3   4   5\n",
       "2018-01-02   6   7   8   9  10  11\n",
       "2018-01-03  12  13  14  15  16  17\n",
       "2018-01-04  18  19  20  21  22  23\n",
       "2017-01-01   0   1   2   3   4   5\n",
       "2017-01-02   6   7   8   9  10  11\n",
       "2017-01-03  12  13  14  15  16  17\n",
       "2017-01-04  18  19  20  21  22  23"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_pickle('study.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   6   7   8   9  10  11\n",
       "0  2018-01-01   0   1   2   3   4   5\n",
       "1  2018-01-02   6   7   8   9  10  11\n",
       "2  2018-01-03  12  13  14  15  16  17\n",
       "3  2018-01-04  18  19  20  21  22  23\n",
       "4  2017-01-01   0   1   2   3   4   5\n",
       "5  2017-01-02   6   7   8   9  10  11\n",
       "6  2017-01-03  12  13  14  15  16  17\n",
       "7  2017-01-04  18  19  20  21  22  23"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('study.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key':['k0','k1','k2'],\n",
    "                   'A':['A0','A1','A2']})\n",
    "right = pd.DataFrame({'key':['k0','k1','k2'],\n",
    "                    'B':['b1','b2','b3']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>key</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>k0</td>\n",
       "      <td>b1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>k1</td>\n",
       "      <td>b2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>k2</td>\n",
       "      <td>b3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A key   B\n",
       "0  A0  k0  b1\n",
       "1  A1  k1  b2\n",
       "2  A2  k2  b3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left,right,on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n",
    "                      'key2': ['K0', 'K1', 'K0', 'K1'],\n",
    "                      'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                      'B': ['B0', 'B1', 'B2', 'B3']})\n",
    "right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n",
    "                       'key2': ['K0', 'K0', 'K0', 'K0'],\n",
    "                       'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                       'D': ['D0', 'D1', 'D2', 'D3']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B key1 key2\n",
      "0  A0  B0   K0   K0\n",
      "1  A1  B1   K0   K1\n",
      "2  A2  B2   K1   K0\n",
      "3  A3  B3   K2   K1\n",
      "    C   D key1 key2\n",
      "0  C0  D0   K0   K0\n",
      "1  C1  D1   K1   K0\n",
      "2  C2  D2   K1   K0\n",
      "3  C3  D3   K2   K0\n"
     ]
    }
   ],
   "source": [
    "print(left)\n",
    "print(right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B key1 key2   C   D\n",
      "0  A0  B0   K0   K0  C0  D0\n",
      "1  A2  B2   K1   K0  C1  D1\n",
      "2  A2  B2   K1   K0  C2  D2\n"
     ]
    }
   ],
   "source": [
    "res = pd.merge(left,right,on=['key1','key2'])\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    A   B key1 key2    C    D\n",
      "0  A0  B0   K0   K0   C0   D0\n",
      "1  A1  B1   K0   K1  NaN  NaN\n",
      "2  A2  B2   K1   K0   C1   D1\n",
      "3  A2  B2   K1   K0   C2   D2\n",
      "4  A3  B3   K2   K1  NaN  NaN\n"
     ]
    }
   ],
   "source": [
    "res = pd.merge(left,right,on=['key1','key2'],how='left')\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})\n",
    "df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   col1 col_left\n",
      "0     0        a\n",
      "1     1        b\n",
      "   col1  col_right\n",
      "0     1          2\n",
      "1     2          2\n",
      "2     2          2\n"
     ]
    }
   ],
   "source": [
    "print(df1)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col_left</th>\n",
       "      <th>col_right</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1 col_left  col_right      _merge\n",
       "0     0        a        NaN   left_only\n",
       "1     1        b        2.0        both\n",
       "2     2      NaN        2.0  right_only\n",
       "3     2      NaN        2.0  right_only"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on='col1', how='outer', indicator=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col_left</th>\n",
       "      <th>col_right</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1 col_left  col_right     columns\n",
       "0     0        a        NaN   left_only\n",
       "1     1        b        2.0        both\n",
       "2     2      NaN        2.0  right_only\n",
       "3     2      NaN        2.0  right_only"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on='col1', how='outer', indicator='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
    "                     'B': ['B0', 'B1', 'B2']},\n",
    "                     index=['K0', 'K1', 'K2'])\n",
    "right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
    "                      'D': ['D0', 'D2', 'D3']},\n",
    "                     index=['K0', 'K2', 'K3'])\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = pd.merge(left, right, left_index=True, right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A   B   C   D\n",
      "K0  A0  B0  C0  D0\n",
      "K2  A2  B2  C2  D2\n"
     ]
    }
   ],
   "source": [
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = pd.merge(left, right, left_index=True, right_index=True, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]})\n",
    "girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age   k\n",
      "0    1  K0\n",
      "1    2  K1\n",
      "2    3  K2\n",
      "   age   k\n",
      "0    4  K0\n",
      "1    5  K0\n",
      "2    6  K3\n"
     ]
    }
   ],
   "source": [
    "print(boys)\n",
    "print(girls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girl'], how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age_boy   k  age_girl\n",
      "0        1  K0         4\n",
      "1        1  K0         5\n"
     ]
    }
   ],
   "source": [
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.Series(np.random.randn(1000),index=np.arange(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd75d2bfe80>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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05o72RA23yCIC4nsP7zgaqyocULOxrnx8Ob725EqYTb8w/rtbPUrurxRCuXzW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\nvOX8iTGVsrn0/GeNUrpc3X3JVHVc6a8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd75d3bf908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.694534</td>\n",
       "      <td>1.026675</td>\n",
       "      <td>-1.455653</td>\n",
       "      <td>-0.182077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.125398</td>\n",
       "      <td>0.778236</td>\n",
       "      <td>-0.463712</td>\n",
       "      <td>0.689644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.011084</td>\n",
       "      <td>0.597370</td>\n",
       "      <td>-2.199593</td>\n",
       "      <td>0.126941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.723792</td>\n",
       "      <td>0.406804</td>\n",
       "      <td>-2.909697</td>\n",
       "      <td>1.660607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.420905</td>\n",
       "      <td>0.617127</td>\n",
       "      <td>-3.021962</td>\n",
       "      <td>1.439068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.894395</td>\n",
       "      <td>-0.944641</td>\n",
       "      <td>-3.610213</td>\n",
       "      <td>2.083898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.619797</td>\n",
       "      <td>0.492541</td>\n",
       "      <td>-3.696832</td>\n",
       "      <td>2.257589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.504896</td>\n",
       "      <td>0.153758</td>\n",
       "      <td>-2.226328</td>\n",
       "      <td>1.145204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.603527</td>\n",
       "      <td>1.521025</td>\n",
       "      <td>-1.550388</td>\n",
       "      <td>0.198303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.207760</td>\n",
       "      <td>3.925029</td>\n",
       "      <td>-0.388188</td>\n",
       "      <td>-0.467003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.437298</td>\n",
       "      <td>3.948069</td>\n",
       "      <td>-0.191537</td>\n",
       "      <td>-0.403163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.169460</td>\n",
       "      <td>3.772713</td>\n",
       "      <td>-0.146631</td>\n",
       "      <td>0.505005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.332238</td>\n",
       "      <td>3.788691</td>\n",
       "      <td>0.166550</td>\n",
       "      <td>1.107614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.247027</td>\n",
       "      <td>3.045208</td>\n",
       "      <td>1.896046</td>\n",
       "      <td>0.731401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.141845</td>\n",
       "      <td>4.168511</td>\n",
       "      <td>1.245773</td>\n",
       "      <td>-0.514936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.732323</td>\n",
       "      <td>4.112712</td>\n",
       "      <td>1.335728</td>\n",
       "      <td>0.331207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.344436</td>\n",
       "      <td>4.449576</td>\n",
       "      <td>2.543634</td>\n",
       "      <td>-0.937111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.791026</td>\n",
       "      <td>5.202936</td>\n",
       "      <td>4.481067</td>\n",
       "      <td>-1.739011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.404018</td>\n",
       "      <td>6.496176</td>\n",
       "      <td>4.458937</td>\n",
       "      <td>-3.782805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.136623</td>\n",
       "      <td>5.216651</td>\n",
       "      <td>3.791768</td>\n",
       "      <td>-3.169851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.346268</td>\n",
       "      <td>4.500945</td>\n",
       "      <td>4.182076</td>\n",
       "      <td>-4.101989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.218055</td>\n",
       "      <td>5.123541</td>\n",
       "      <td>4.850622</td>\n",
       "      <td>-4.235026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.498948</td>\n",
       "      <td>4.486264</td>\n",
       "      <td>2.969487</td>\n",
       "      <td>-4.556698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.868777</td>\n",
       "      <td>2.328321</td>\n",
       "      <td>2.500446</td>\n",
       "      <td>-3.660914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.064618</td>\n",
       "      <td>3.065259</td>\n",
       "      <td>3.733933</td>\n",
       "      <td>-5.867883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.785586</td>\n",
       "      <td>3.448009</td>\n",
       "      <td>3.676562</td>\n",
       "      <td>-5.447232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.568120</td>\n",
       "      <td>2.555710</td>\n",
       "      <td>3.368359</td>\n",
       "      <td>-5.958103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-1.090035</td>\n",
       "      <td>3.005364</td>\n",
       "      <td>3.973561</td>\n",
       "      <td>-4.493626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-1.534343</td>\n",
       "      <td>3.342064</td>\n",
       "      <td>5.555718</td>\n",
       "      <td>-5.583420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-2.082689</td>\n",
       "      <td>4.755123</td>\n",
       "      <td>6.670453</td>\n",
       "      <td>-5.025820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>8.573459</td>\n",
       "      <td>30.878279</td>\n",
       "      <td>-44.826174</td>\n",
       "      <td>-35.108323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>8.882346</td>\n",
       "      <td>32.134427</td>\n",
       "      <td>-45.700129</td>\n",
       "      <td>-34.282163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>9.685546</td>\n",
       "      <td>31.873808</td>\n",
       "      <td>-46.601033</td>\n",
       "      <td>-32.992077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>8.950233</td>\n",
       "      <td>31.290999</td>\n",
       "      <td>-47.303380</td>\n",
       "      <td>-33.043869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>6.605881</td>\n",
       "      <td>32.133608</td>\n",
       "      <td>-47.271347</td>\n",
       "      <td>-34.401756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>6.154263</td>\n",
       "      <td>31.137199</td>\n",
       "      <td>-47.276422</td>\n",
       "      <td>-36.610888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>5.009400</td>\n",
       "      <td>31.878904</td>\n",
       "      <td>-48.601194</td>\n",
       "      <td>-38.532397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>5.677469</td>\n",
       "      <td>31.386408</td>\n",
       "      <td>-50.319116</td>\n",
       "      <td>-39.069544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>4.546472</td>\n",
       "      <td>31.596790</td>\n",
       "      <td>-51.801820</td>\n",
       "      <td>-38.794540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>5.334853</td>\n",
       "      <td>28.986358</td>\n",
       "      <td>-53.188886</td>\n",
       "      <td>-38.179131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>4.046785</td>\n",
       "      <td>30.482918</td>\n",
       "      <td>-53.552518</td>\n",
       "      <td>-38.277898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>3.360647</td>\n",
       "      <td>30.086593</td>\n",
       "      <td>-52.338106</td>\n",
       "      <td>-38.962408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>4.824835</td>\n",
       "      <td>31.001565</td>\n",
       "      <td>-50.785029</td>\n",
       "      <td>-37.819364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>4.624444</td>\n",
       "      <td>34.285812</td>\n",
       "      <td>-50.273259</td>\n",
       "      <td>-39.307374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>3.813628</td>\n",
       "      <td>34.780673</td>\n",
       "      <td>-48.564431</td>\n",
       "      <td>-40.662688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>3.863806</td>\n",
       "      <td>34.479876</td>\n",
       "      <td>-49.671713</td>\n",
       "      <td>-40.494623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>4.874060</td>\n",
       "      <td>35.098600</td>\n",
       "      <td>-50.813233</td>\n",
       "      <td>-40.422962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>5.120290</td>\n",
       "      <td>34.547158</td>\n",
       "      <td>-52.203992</td>\n",
       "      <td>-40.144717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>4.732344</td>\n",
       "      <td>34.498197</td>\n",
       "      <td>-52.857583</td>\n",
       "      <td>-41.187018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>5.659987</td>\n",
       "      <td>32.906842</td>\n",
       "      <td>-52.948773</td>\n",
       "      <td>-41.664749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>6.878470</td>\n",
       "      <td>30.961027</td>\n",
       "      <td>-52.814837</td>\n",
       "      <td>-42.320592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>7.095610</td>\n",
       "      <td>31.072221</td>\n",
       "      <td>-50.729504</td>\n",
       "      <td>-42.283757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>8.126671</td>\n",
       "      <td>31.383152</td>\n",
       "      <td>-50.880699</td>\n",
       "      <td>-42.074961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>8.247973</td>\n",
       "      <td>29.383233</td>\n",
       "      <td>-50.962759</td>\n",
       "      <td>-42.922583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>7.359719</td>\n",
       "      <td>30.685366</td>\n",
       "      <td>-53.235699</td>\n",
       "      <td>-44.247804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>7.000694</td>\n",
       "      <td>30.213681</td>\n",
       "      <td>-53.534690</td>\n",
       "      <td>-45.216904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>7.437527</td>\n",
       "      <td>30.564791</td>\n",
       "      <td>-51.612256</td>\n",
       "      <td>-45.696277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>8.365733</td>\n",
       "      <td>29.722355</td>\n",
       "      <td>-51.946321</td>\n",
       "      <td>-43.738791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>8.360565</td>\n",
       "      <td>30.079992</td>\n",
       "      <td>-52.775884</td>\n",
       "      <td>-43.790596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>6.666019</td>\n",
       "      <td>29.186010</td>\n",
       "      <td>-53.942908</td>\n",
       "      <td>-42.842080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            A          B          C          D\n",
       "0    0.694534   1.026675  -1.455653  -0.182077\n",
       "1    1.125398   0.778236  -0.463712   0.689644\n",
       "2    1.011084   0.597370  -2.199593   0.126941\n",
       "3    0.723792   0.406804  -2.909697   1.660607\n",
       "4    1.420905   0.617127  -3.021962   1.439068\n",
       "5    1.894395  -0.944641  -3.610213   2.083898\n",
       "6    1.619797   0.492541  -3.696832   2.257589\n",
       "7    1.504896   0.153758  -2.226328   1.145204\n",
       "8    1.603527   1.521025  -1.550388   0.198303\n",
       "9    0.207760   3.925029  -0.388188  -0.467003\n",
       "10   0.437298   3.948069  -0.191537  -0.403163\n",
       "11  -0.169460   3.772713  -0.146631   0.505005\n",
       "12   0.332238   3.788691   0.166550   1.107614\n",
       "13  -0.247027   3.045208   1.896046   0.731401\n",
       "14  -0.141845   4.168511   1.245773  -0.514936\n",
       "15  -0.732323   4.112712   1.335728   0.331207\n",
       "16   0.344436   4.449576   2.543634  -0.937111\n",
       "17   1.791026   5.202936   4.481067  -1.739011\n",
       "18   2.404018   6.496176   4.458937  -3.782805\n",
       "19   2.136623   5.216651   3.791768  -3.169851\n",
       "20   1.346268   4.500945   4.182076  -4.101989\n",
       "21   1.218055   5.123541   4.850622  -4.235026\n",
       "22   1.498948   4.486264   2.969487  -4.556698\n",
       "23   1.868777   2.328321   2.500446  -3.660914\n",
       "24   2.064618   3.065259   3.733933  -5.867883\n",
       "25   0.785586   3.448009   3.676562  -5.447232\n",
       "26  -0.568120   2.555710   3.368359  -5.958103\n",
       "27  -1.090035   3.005364   3.973561  -4.493626\n",
       "28  -1.534343   3.342064   5.555718  -5.583420\n",
       "29  -2.082689   4.755123   6.670453  -5.025820\n",
       "..        ...        ...        ...        ...\n",
       "970  8.573459  30.878279 -44.826174 -35.108323\n",
       "971  8.882346  32.134427 -45.700129 -34.282163\n",
       "972  9.685546  31.873808 -46.601033 -32.992077\n",
       "973  8.950233  31.290999 -47.303380 -33.043869\n",
       "974  6.605881  32.133608 -47.271347 -34.401756\n",
       "975  6.154263  31.137199 -47.276422 -36.610888\n",
       "976  5.009400  31.878904 -48.601194 -38.532397\n",
       "977  5.677469  31.386408 -50.319116 -39.069544\n",
       "978  4.546472  31.596790 -51.801820 -38.794540\n",
       "979  5.334853  28.986358 -53.188886 -38.179131\n",
       "980  4.046785  30.482918 -53.552518 -38.277898\n",
       "981  3.360647  30.086593 -52.338106 -38.962408\n",
       "982  4.824835  31.001565 -50.785029 -37.819364\n",
       "983  4.624444  34.285812 -50.273259 -39.307374\n",
       "984  3.813628  34.780673 -48.564431 -40.662688\n",
       "985  3.863806  34.479876 -49.671713 -40.494623\n",
       "986  4.874060  35.098600 -50.813233 -40.422962\n",
       "987  5.120290  34.547158 -52.203992 -40.144717\n",
       "988  4.732344  34.498197 -52.857583 -41.187018\n",
       "989  5.659987  32.906842 -52.948773 -41.664749\n",
       "990  6.878470  30.961027 -52.814837 -42.320592\n",
       "991  7.095610  31.072221 -50.729504 -42.283757\n",
       "992  8.126671  31.383152 -50.880699 -42.074961\n",
       "993  8.247973  29.383233 -50.962759 -42.922583\n",
       "994  7.359719  30.685366 -53.235699 -44.247804\n",
       "995  7.000694  30.213681 -53.534690 -45.216904\n",
       "996  7.437527  30.564791 -51.612256 -45.696277\n",
       "997  8.365733  29.722355 -51.946321 -43.738791\n",
       "998  8.360565  30.079992 -52.775884 -43.790596\n",
       "999  6.666019  29.186010 -53.942908 -42.842080\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas = pd.DataFrame(np.random.randn(1000,4),index = np.arange(1000),columns=list('ABCD'))\n",
    "datas.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd75d53c0f0>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PHLb5dDdh5mo7PsiSysrIE0K3AM++9z3pGUXwRAv68u7GClz2giw3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8kWg0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\noRiGKOGvUCgUw5D/D/vV+WBvLU/EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd75d53c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YpAX8MtsmegknwjS/+jNauiepE8XvHIen/gu5z7HaQ8OUWcL5uVzqMcbjxVnp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\nMdAGQaPRaDQA/H8Lda9rMu857gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd75d526be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame(\n",
    "    np.random.randn(1000,4),\n",
    "    index=np.arange(1000),\n",
    "    columns=list(\"ABCD\")\n",
    "    )\n",
    "data.cumsum()\n",
    "axs = data.plot.scatter(x='A', y='C',color = 'red',label = 'Class 1')\n",
    "data.plot.scatter(x='A', y='B',color = 'Green',label = 'Class 2',ax = axs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd75d6c04a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.DataFrame(np.random.randn(10))\n",
    "data.plot.bar(label = 'text')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3A58DVgHXVtUDSa4GdlfVTuA3kmwGjgLfAd7UVT2SpLl1FgoAVbUL2DVl2VV90+8E3tll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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd753b98cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.hist(label='text')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0zYAyTScqPJQrB3fis5xdFOwrdTqOqYdtSmD+6++fbyAmMowbrFVhHHD1kM6E\niPCGtS58khULA8C6nYeYs6ZmrMJaFcYJ7eOiuKBPB6YuLaC4rNLpOKYOKxYGsFaF8Q2Tz+pCSXkV\nby/e6nQUU4cVC1PTqli7g+vOsFaFcVbvxDiGdYvntQX5lFfZRXq+xIqF4R9fbCA6PNRaFcYnTD6r\nC7sPlfP+So8XqTZNwIpFkMvbXcJHa3ZwzRkptLI1oIwPOLN7PGkdYnnpm024bK8Ln2HFIshN+TKP\nqLBQbrRWhfERIsJNZ3chb3cJX+TudjqOcbNiEcQ27znM+yu3cfWQTrSJiXQ6jjH/NaZPBxJbNuOl\nbzY5HcW4WbEIYs99lUd4aAg/P6uL01GM+Z7w0BBuGJbKks37WLZlv9NxDFYsglbBvlLeW76NSYM7\n0bZFlNNxjPmBywcl0yo6nGe/2OB0FIMVi6D1/NcbCRHhprO7Oh3FmHo1jwzjxjO78OW6IlYXHnA6\nTtCzYhGEth84wvTMAiZmJNE+zloVxnddc3pn4pqF8/fP85yOEvSsWAShF7/eiCr830+sVWF8W4uo\ncG4YlspnObtYu+2g03GCmhWLILO7uIx3lhZwyalJJLWKdjqOMQ269owUWkSF8Q8bu3CUFYsg89I3\nm6h2Kb8cbq0K4x/imoVz/dBUPsnaRc6OYqfjBC2vFgsRGS0i60QkT0Tuqef1s0RkuYhUicildV67\nVkQ2uG/XejNnsNhTUs6/F29hXL+OdG7T3Ok4xnjshqGpxESG8ewXNnbhFK8VCxEJBaYA5wPpwCQR\nSa9z2FbgOuDtOue2Bh4ETgMGAw+KSCtvZQ0Wr8zPp7zKxc3ndHM6ijHHJS46nOvOSOGjNTvI2m5j\nF07wZstiMJCnqptUtQKYCoyrfYCqblbV1YCrzrmjgHmquk9V9wPzgNFezBrw9h+u4M2Fm7mwb0e6\nJsQ4HceY4/bzs7oQ1yycP32yzukoQcmbxSIRKKj1uND9nLfPNfV47dt8DldUc8twa1UY/xTXLJyb\nzu7Kl+uKWJK/z+k4QcebxULqec7TJSQ9OldEJotIpohkFhUVHVe4YHLwSCWvf7uZ0b3a07N9C6fj\nGHPCrjsjhbYtInn641xUbUXapuTNYlEIJNd6nAR4ukC9R+eq6kuqmqGqGQkJCSccNND989t8DpVX\ncYuNVRg/1ywilNtHdCdzy35bkbaJebNYLAW6i0iqiEQAVwCzPTz3E2CkiLRyD2yPdD9njtPB0kpe\nnZ/PyPR29E6MczqOMSftsoxkUtpE88dP1tl+F03Ia8VCVauAW6j5JZ8DTFPVLBF5RETGAojIIBEp\nBCYCL4pIlvvcfcCj1BScpcBBSyJ0AAAQo0lEQVQj7ufMcXp5/iYOlVdxx3k9nI5iTKMIDw3h1yN7\nkrvzEO+v2uZ0nKAhgdLvl5GRoZmZmU7H8Cn7Dldw5h++4Cc92zLlqlOdjmNMo3G5lLFTFrD/cCWf\n//psosJDnY7kt0RkmapmNHScXcEdwF78ZiOlldX8akR3p6MY06hCQoT7L0hn24EjvGwbJDUJKxYB\nquhQOf/6bgtj+3WkezubAWUCz5AubTi/d3ue+2ojOw+WOR0n4FmxCFAvfr2R8qpqbj/XWhUmcN07\nJo1ql/L0x7lORwl4ViwC0K7iMt5ctIUJA5LoYldrmwCW3DqaG85M5b0V21hZYBskeZMViwD0/Fcb\nqXIpt51r11WYwHfz8G7Ex0TyyAdZdqGeF1mxCDCF+0t5e/FWJg5MspVlTVCIiQzjt6N6snzrAWau\nsKm03mLFIsA8M289InCbjVWYIHLpwCT6J7fk8Y9yOFBa4XScgGTFIoDk7Chm5optXDc0hY4tmzkd\nx5gmExIiPDGhDweOVPKHj21VWm+wYhFAnv44lxaRYfzybBurMMEnvWMs15+RwjtLtrJsiy340Nis\nWASIhRv38uW6Im4e3o246HCn4xjjiDvO60GHuCjum7mWyuq62+SYk2HFIgCoKk99nEuHuCiuPSPF\n6TjGOKZ5ZBgPje1F7s5DvLYg3+k4AcWKRQCYu3YnqwoOcMd5PWyNHBP0RvVqz4i0dvzls/Vs2XvY\n6TgBw4qFnyuvquapubn0aBfDJacmOR3HGJ/w6PhehIeG8JsZq20Z80ZixcLPvbZgM1v3lfLAhb0I\nDalvg0Fjgk+HuGb8/sJ0luTv442Fm52OExCsWPix3cVlPPvFBs5Lb8ew7vFOxzHGp0wcmMTwngn8\n4eNcNu+x7qiTZcXCjz39yToqq5X7xqQ5HcUYnyMiPHlxX3d31CrrjjpJViz81KqCA8xYVsjPhqWS\nEm/LehhTn/ZxUTxwYTpLN+/n5fm278XJsGLhh1wu5eEPsoiPieSWc+wCPGOO5dKBSYzu1Z4/fbqO\nNYUHnY7jt6xY+KFpmQUs33qAu0f3JCYyzOk4xvg0EeGpS/oQHxPJbVNXcLi8yulIfsmKhZ/ZU1LO\nk3NzGZzamksH2lRZYzzRMjqCv1zen817D/PQ7Cyn4/glKxZ+5vGPciitqOKJCX0QsamyxnhqSJc2\n3Dq8G9OXFTJ71Xan4/gdrxYLERktIutEJE9E7qnn9UgR+Y/79cUikuJ+PkVEjojISvftBW/m9BcL\nNuxh5opt/N/ZXenW1nbAM+Z43XZudzI6t+J3764mb/chp+P4Fa8VCxEJBaYA5wPpwCQRSa9z2A3A\nflXtBvwF+EOt1zaqan/37SZv5fQXRyqquX/WGlLaRPPL4TaobcyJCAsN4dkrT6VZRCi/eHMZJTZ+\n4TFvtiwGA3mquklVK4CpwLg6x4wD3nDfnwGcK9a3Uq8/frKOzXtLeXxCH1v/yZiT0D4uin9MOpXN\ne0v5zfRVthWrh7xZLBKBglqPC93P1XuMqlYBB4E27tdSRWSFiHwtImd6MafPW7RpL//8Lp9rTu/M\n0G52pbYxJ+v0rm24Z/QpzF27k5e+sesvPOHNeZf1tRDqlvAfO2YH0ElV94rIQGCWiPRS1eLvnSwy\nGZgM0KlTp0aI7HsOl1fxmxmr6NQ6mnvOP8XpOMYEjBvPTGVFwX7+8HEuPdq1YPgpbZ2O5NO82bIo\nBJJrPU4C6k5B+O8xIhIGxAH7VLVcVfcCqOoyYCPQo+4HqOpLqpqhqhkJCQle+Cc474k5ORTuP8Kf\nJvYjOsKuqTCmsYgIf7y0H2kdYrnl7eXk7Chu+KQg5s1isRToLiKpIhIBXAHMrnPMbOBa9/1LgS9U\nVUUkwT1Ajoh0AboDQddW/Cx7F28t3sqNw1IZlNLa6TjGBJzmkWG8eu0gWkSFc8PrS9ldXOZ0JJ/l\ntWLhHoO4BfgEyAGmqWqWiDwiImPdh70KtBGRPOBO4Oj02rOA1SKyipqB75tUNag21d124Ai/nr6K\n3omx3DWqp9NxjAlY7eOieOXaDA4cqeTGf2VSWmEzpOojgTITICMjQzMzM52O0Sgqq11c8dIi1u08\nxIe3DrOFAo1pAp9l72Lym5mc1SOBl36aQURYcFyzLCLLVDWjoeOC43/Dzzwzbz3LtuzniYv7WKEw\npomMSG/H4xP68NW6Iu6ctpJqW9L8e2zE1Md8tHoHz3+1kUmDOzG2X0en4xgTVCYN7kTxkUqenJtL\ni6gwW1anFisWPiRr+0Humr6KgZ1b8dDYuhe7G2Oawi/O7kpxWSVTvtxITGQY945Js4KBFQufsaek\nnMn/WkbL6HBeuHogkWF2lbYxTrlrZE9Kyqp4eX4+ldXKgxelB33BsGLhA0orqrjxjUz2lJQz46Yz\nSGgR6XQkY4KaiPDQ2F6EhoTw2rf5VFS7eGxcb0JCgrdgWLFwWGW1i//793JWFx7g+asH0icpzulI\nxhhqCsbvL0wjMjyE57/aSEWVi6cu7kNYaHDOC7Ji4SCXS/ntjNV8vb6IJy/uw6he7Z2OZIypRUT4\n7aieRIaF8NfPNrC3pJxnrzyV5kG4Q2Vwlkgf4HIp981ay8wV2/j1eT2YNDgw17Yyxt+JCL8a0YPH\nJ/Tm6/VFXPbiQnYF4ZXeViwc4HIp97y3mneWbOX/ftKVW86x/SmM8XVXndaZV68dRP6ew0yY8m3Q\nrSVlxaKJVVW7uGvGKqZlFnLbOd347aieQT/Lwhh/MfyUtkz7xelUqzLhuW+ZuaLQ6UhNxopFEyop\nr+KGNzJ5b/k27hjRgztHWqEwxt/0Tozjg1uH0S+pJXf8ZxX3z1pDeVW107G8zopFE9l5sIyJLyxk\nQd4enry4D7eP6O50JGPMCWrbIoq3bjyNyWd14d+LtjLxhYXk7S5xOpZXWbFoAos27eWiZxewde9h\nXrtukA1mGxMAwkJDuHdMGi9cPZCt+0q54O/z+ee3+bgCdE0pKxZe5HIpz32Vx5UvL6JFZBjv/vIM\nzu4RmJs0GROsRvduz6e/Ooszurbh4Q+yufKVReTtPuR0rEZnS5R7ScG+Un733hoW5O3hgr4deOri\nPrSICnc6ljHGS1SVqUsLeHJODkcqq7lhWBduO7ebz+9w6ekS5VYsGpnLpby9ZCtPzskB4Hdj0rjq\ntE42kG1MkNhTUs6Tc3J5d3khHeOiuOO8HkwYkOizV35bsXDA0s37ePTDbFYXHmRYt3ieuqQPSa2i\nHc1kjHHG0s37eOSDbNZsO0jXhOb8emRPRvdq73PrS1mxaEK5O4v5++cbmLNmJ+1jo7j7/J6M759o\nrQljgpyq8knWTv706XrydpfQvW0MN56Zyrj+iUSF+8bK0lYsmsDKggNM+TKPedm7aB4Ryo1nduEX\nZ3fx+T5KY0zTqnYpH6zazovfbCJnRzFtmkdw1WmdmJiRTHJrZ3sfrFh4SUl5FR+s2s7bi7eyZttB\n4pqFc90ZKVw/NIWW0RFe/3xjjP9SVRZu3MvL8zfx1foiVGFIl9ZcOjCZ89LaERfd9JNgrFg0osPl\nVXyRu5uP1+7ky3W7Ka2opme7FkwanMwlA5NslpMx5rhtO3CEmcsLmbGskM17SwkLEYZ0acN56e04\nN61tk413WrE4CVXVLtZsO8jCTXtZuHEvS/L3UV7lIj4mgpG92nPpwCQGJLe0MQljzElTVVYUHGBe\n9i4+zdrJxqLDACS3bsaQ1DYM6dKGQSmtSW7dzCu/c3yiWIjIaOBvQCjwiqo+Vef1SOBfwEBgL3C5\nqm52v/Y74AagGrhNVT851medaLGoqnaxYXcJa7cdJGt7MVnbD5K9vZjDFTVrvfRs14Kh3eIZ1asd\nGSmtCfWxmQzGmMCSt7uE+RuKWLRpL4vz93GgtBKA2Kgw0jvG0qtjHOkdYumS0Jwu8TEn3XXleLEQ\nkVBgPXAeUAgsBSapanatY34J9FXVm0TkCmCCql4uIunAO8BgoCPwGdBDVX90ta4TLRY7Dh7h9Ce/\nACA6IpS0DrH06hjL4NTWDOnShvgY2+LUGOMMl0vJ3XmIlQUHyNpe8wdt7s5iyipd/z2mVXQ4w7on\n8I9JA07oMzwtFt6ctjMYyFPVTe5AU4FxQHatY8YBD7nvzwCelZp21jhgqqqWA/kikud+v4WNHbJ9\nbBT/mDSAtA6xpMY3t5aDMcZnhIQI6R1jSe8Y+9/nqqpdbNlXSn7RYTbvPcymPYdp1QQD494sFolA\nQa3HhcBpP3aMqlaJyEGgjfv5RXXOTaz7ASIyGZgM0KnTiS3OJyJc1K/jCZ1rjDFNLSw0hK4JMXRN\niGnSz/Xm9ef1/Ylet8/rx47x5FxU9SVVzVDVjIQEW6DPGGO8xZvFohBIrvU4Cdj+Y8eISBgQB+zz\n8FxjjDFNxJvFYinQXURSRSQCuAKYXeeY2cC17vuXAl9ozYj7bOAKEYkUkVSgO7DEi1mNMcYcg9fG\nLNxjELcAn1AzdfY1Vc0SkUeATFWdDbwKvOkewN5HTUHBfdw0agbDq4CbjzUTyhhjjHfZRXnGGBPE\nPJ0665sLrBtjjPEpViyMMcY0yIqFMcaYBgXMmIWIFAFbnM5RSzywx+kQx8kyNw3L7H3+lhecy9xZ\nVRu8UC1gioWvEZFMTwaNfIllbhqW2fv8LS/4fmbrhjLGGNMgKxbGGGMaZMXCe15yOsAJsMxNwzJ7\nn7/lBR/PbGMWxhhjGmQtC2OMMQ2yYuFFIvKoiKwWkZUi8qmI+PzGGSLyRxHJdeeeKSItnc7UEBGZ\nKCJZIuISEZ+dTSIio0VknYjkicg9TudpiIi8JiK7RWSt01k8JSLJIvKliOS4vydudzpTQ0QkSkSW\niMgqd+aHnc5UH+uG8iIRiVXVYvf924B0Vb3J4VjHJCIjqVn9t0pE/gCgqnc7HOuYRCQNcAEvAnep\nqs8tEubJNsO+RkTOAkqAf6lqb6fzeEJEOgAdVHW5iLQAlgHjffz/WYDmqloiIuHAAuB2VV3UwKlN\nyloWXnS0ULg1p54NnHyNqn6qqlXuh4uo2UvEp6lqjqquczpHA/67zbCqVgBHtxn2War6DTWrQfsN\nVd2hqsvd9w8BOdSzy6Yv0Rol7ofh7pvP/a6wYuFlIvK4iBQAVwEPOJ3nOP0MmOt0iABR3zbDPv1L\nzN+JSAowAFjsbJKGiUioiKwEdgPzVNXnMluxOEki8pmIrK3nNg5AVe9T1WTgLeAWZ9PWaCiz+5j7\nqNlL5C3nkv6PJ5l9nEdbBZvGISIxwLvAr+q08H2Sqlaran9qWvKDRcTnuv28tvlRsFDVER4e+jbw\nEfCgF+N4pKHMInItcCFwrvrIoNZx/D/7KtsquIm4+/3fBd5S1fecznM8VPWAiHwFjAZ8amKBtSy8\nSES613o4Fsh1KounRGQ0cDcwVlVLnc4TQDzZZticJPdg8atAjqo+43QeT4hIwtFZhyLSDBiBD/6u\nsNlQXiQi7wI9qZmpswW4SVW3OZvq2Nxb3EYCe91PLfKDGVwTgH8ACcABYKWqjnI21Q+JyBjgr/xv\nm+HHHY50TCLyDvATalZD3QU8qKqvOhqqASIyDJgPrKHm5w7gXlWd41yqYxORvsAb1HxfhADTVPUR\nZ1P9kBULY4wxDbJuKGOMMQ2yYmGMMaZBViyMMcY0yIqFMcaYBlmxMMYY0yArFsYYYxpkxcIYY0yD\nrFgYY4xp0P8DRkK9J2C6GvEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd74b682a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot.kde(label='text')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
